Text summarization aims to condense long documents and retain key information. Critical to
the success of a summarization model is the faithful inference of latent representations of …

Recently, neural topic models (NTMs) have been incorporated into pre-trained language
models (PLMs), to capture the global semantic information for text summarization. However …

Transformers have become the state-of-the-art architectures for various tasks in Natural
Language Processing (NLP) and Computer Vision (CV); however, their space and …

Query-focused summarization (QFS) aims to produce summaries that answer particular
questions of interest, enabling greater user control and personalization. While recently …

Companies invest a substantial amount of time and resources in ensuring the compliance to
the existing regulations or in the form of fines when compliance cannot be proven in auditing …

For text summarization, the role of discourse structure is pivotal in discerning the core
content of a text. Regrettably, prior studies on incorporating Rhetorical Structure Theory …

In the field of text summarization, extractive techniques aim to extract key sentences from a
document to form a summary. However, traditional methods are not sensitive enough to …

Labeling large amounts of extractive summarization data is often prohibitive expensive due
to time, financial, and expertise constraints, which poses great challenges to incorporating …

Scientific paper summarization aims at generating a short and concise digest while
preserving important information of the original document. Currently, scientific paper …

The extractive summarization approach involves selecting the source document's salient
sentences to build a summary. One of the most important aspects of extractive …